Electrocardiogram Beat Classification Using BAT-Optimized Fuzzy KNN Classifier
Keyword(s):
In this chapter, the BAT-optimized fuzzy k-nearest neighbor (FKNN-BAT) algorithm is proposed for discrimination of the electrocardiogram (ECG) beats. The five types of beats (i.e., normal [N], right bundle block branch [RBBB], left bundle block branch [LBBB], atrial premature contraction [APC], and premature ventricular contraction [PVC]) are taken from MIT-BIH arrhythmia database for the experimentation. Thereafter, the features are extracted from five type of beats and fed to the proposed BAT-tuned fuzzy KNN classifier. The proposed classifier achieves the overall accuracy of 99.88%.
2020 ◽
Vol 10
(2)
◽
pp. 1833
2020 ◽
Keyword(s):
2014 ◽
Vol 8
(1)
◽
pp. 16-26
◽
Keyword(s):
2012 ◽
Vol 532-533
◽
pp. 1455-1459